Jesper Mazanti Aaslyng

Summary We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from a canopy using the smooth fractionator, (ii) sampling of leaves from the selected plants using the fractionator, and (iii) area estimation of the sampled leaves using point counting. We apply this procedure to estimate the total area of a chrysanthemum (Chrysanthemum morifolium L.) canopy and evaluate both the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting is high. Using a grid intensity of 1.76 cm²/point we estimated plant and canopy surface areas with accuracies similar to or better than those obtained using image analysis and a commercial leaf area meter. For canopy surface areas of approximately 1 m² (10 plants), the fractionator leaf approach with sampling fraction equal to 1/9 followed by point counting using a 4.3 cm²/point grid produced a coefficient of error of less than 7%. The smooth fractionator can be used to ensure that the additional contribution to the estimator variance due to between‐plant variability is small.

There is increasing use of electricity for supplemental lighting in the northern European greenhouse industry. One reason for this may be to secure a high growth rate during low-light periods by an attempt to increase net photosynthesis. We wanted to clarify which period of the day resulted in the best use of a 5-h supplemental light period for photosynthesis and growth. The periods tested were supplemental light during the night, day, morning, and evening. The experiments were carried out in daylight climate chambers measuring canopy gas exchange. The air temperature was 25 °C and the CO ₂ level ≈900 ppm. Vegetative chrysanthemum was used, because this species responds quickly to change in light level. The leaf areas of the plant canopies were nondestructively measured each week during the 4-week experimental period. The fact that the quantum yield of photosynthesis is greater at low than at high light intensities favors the use of supplemental light during the dark period, but growth measured as dry weight of the treated plants at the end of the experiments was not significantly different given identical light integrals of the treatments. However, one experiment indicated that increased time with dark hours during day and night (24 h) might decrease net photosynthesis. The assimilation per unit leaf area was approximately the same during times of sunlight through a diffusing screen at 100 μmol·m ⁻² ·s ⁻¹ of photosynthetic photon flux ( PPF ) as during times of supplemental (direct) light application at PPF of 200 μmol·m ⁻² ·s ⁻¹ by high-pressure sodium lamps. We conclude that during the winter and periods of low light intensities, the daily carbon gain does not depend on the time of supplemental light application, but is linked to the total light integral. However, extended time with dark hours during day and night (24 h) might be a disadvantage because of longer periods with dark respiration and subsequent loss of carbon. Our results indicate that during times of low light conditions, it is not necessary to include factors such as the timing of supplemental lighting application to achieve higher net photosynthesis in climate control strategies.

ISHS International Symposium on High Technology for Greenhouse System Management: Greensys2007 APPLICATION OF A PHOTOSYNTHESIS MODEL TO OPTIMIZE GREENHOUSE CLIMATE MANAGEMENT

To study whether root-zone heating during cold nights could increase root growth and nutrient uptake, plants of Chrysanthemum morifolium were grown in nutrient solution cultures at a day temperature (DT) of 20°C, and four combinations of night air temperatures (NAT) and night root-zone temperatures (NRT) in a controlled climate chamber experiment. The NAT/NRT temperatures of the four treatments were 8°/8°C, 8°/20°C, 20°/8°C and 20°/20ºC, respectively. Plants grown at 8°C NAT had increased starch accumulation in the leaves, which explained the increase in leaf dry matter (DM), and which occurred at the expense of stem and root DM. Furthermore, plants grown at 8°C NAT had fewer leaves, reduced leaf area, and shorter stems. Root-zone heating of plants grown at 8°C NAT, did not affect total leaf number, or leaf area, but the root length increased. However, this had no effect on NO₃- uptake. Furthermore, root-zone heating did not reduce leaf starch accumulation and the increase in leaf DM. The present results do not support the hypothesis that root-zone heating in chrysanthemum can be used to increase root demand for carbohydrates, and increase the export of carbohydrates from leaves at night, when the night air temperature is 8ºC.

Greenhouse energy-saving and biocide reduction can be achieved through dynamic greenhouse climate control with computerized model-based regimes. This can be optimized when next to greenhouse macroclimate (i.e., the aerial environment) also, the crop microclimate is predicted. The aim of this article was to design and apply a simple deterministic microclimate model for dynamic greenhouse climate control concepts. The model calculates crop temperature and latent heat of evaporation in different vertical levels of a dense canopy of potted plants. The model was validated with data attained from experiments on dynamic or nondynamic (regular) controlled greenhouse cultivation. Crop temperature was with a 95% confidence interval of 2 °C or 2.4 °C for sunlit or shaded leaves, respectively, accurately predicted in a simple greenhouse with predefined climate set points. With a more dynamic greenhouse control also including assimilation lighting and screens, the prediction quality decreased but still had a 95% confidence interval of crop temperature prediction of 3.8 °C for sunlit leaves. Simulations showed that controlling greenhouse temperature according to the predicted crop temperature rather than according to the air temperature can save energy. Energy-saving is highest during winter and 12% energy saving was attained during January under Danish climate conditions.

ISHS V International Symposium on Artificial Lighting in Horticulture SIMULATING DYNAMIC CONTROL OF SUPPLEMENTARY LIGHTING